Preventing Inferences Through Data Dependencies on Sensitive Data

Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Prior work on preventing inference control through data dependencies detect and deny queries which may lead to leakage, or only protect against exact reconstructio...

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Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 36; no. 10; pp. 5308 - 5327
Main Authors Pappachan, Primal, Zhang, Shufan, He, Xi, Mehrotra, Sharad
Format Journal Article
LanguageEnglish
Published IEEE 01.10.2024
Subjects
Access control
data dependencies
Finance
inference control
inference protection
Metadata
Regulation
Remuneration
security & privacy
Semantics
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Summary:Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Prior work on preventing inference control through data dependencies detect and deny queries which may lead to leakage, or only protect against exact reconstruction of the sensitive data. These solutions result in poor utility, and poor security respectively. In this paper, we present a novel security model called full deniability . Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for efficiently implementing full deniability on a given database instance with a set of data dependencies and sensitive cells. Using experiments on two different datasets, we demonstrate that our approach protects against realistic adversaries while hiding only minimal number of additional non-sensitive cells and scales well with database size and sensitive data.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2023.3336630